This visual data story explores what drinks the customers order at my workplace! Because I am actually curious (hehe).
I have tried my best to collect data for every drink order received but because there are busy rushes and also my breaks, I wasn’t able to log/collect data on quite a few orders.
The data has been observed and collected over four of the days I worked on 29th of March, 3rd of May, 4th of May, and 10th of May 2025.
Overall, the customers mostly order coffees and teas.
In this case, the total number of teas and coffees ordered by customers are the same.
But look! The total number of orders we get for each drink type are different for each day.
Remember how the total number of tea and coffee orders were the same in the overall plot? Well we can see that this isn’t the case when we look at the total number of orders for each day. Some days there are more tea orders and other days there are more coffee orders.
In conclusion, it differs!
(Also, pretend the x-axis scales are from 0 to 50. And 29 May says 29 March.)
The majority of the orders were hot drinks and some of the orders were iced drinks.
The outside temperature doesn’t seem to affect whether the customers order hot drinks or iced drinks.
I mean that makes sense though! Most of the drinks we offer are better as a hot drink than as an iced drink (in my opinion haha).
I have animated the plot so that its easier to see!
It looks like the customers order any type of tea regardless of what time it is.
I expected the teas with the highest caffeine content to be more popular in the morning/early afternoon and the teas with the lowest caffeine content to be more popular in the afternoon - more popular implying more orders.
It is the case for traditional black teas as customers didn’t order any past 1PM. But for other tea types, I seem to have assumed wrong!
As for the coffees, it looks like the most coffee orders we got were flat whites and the least coffee orders we got were short blacks.
This might be supporting the fact that the most popular coffee in New Zealand is flat white.
There isn’t much pattern to take away this “time” plot. It looks as though for some coffee types, there are more orders in the morning than in the afternoon.
Maybe if I collected more data and reliably logged all orders, then we might start to see some pattern in when customers order coffee for each coffee types?
Hope you enjoyed reading through my visual data story :D
As you can probably tell from the previous header, I love coffee and I’m a bit of a coffee snob so I’ll leave some pictures of my passion - making coffee and latte art!